(untitled) (Research, Society and Development, 2020)
Objective: To analyze infant death patterns between the years 2000 and 2017 in a city in southern Brazil. Method: From the data extraction from the infant mortality database of the Informatics Department of the Unified Health System (DATASUS), apply data analysis techniques based on the Knowledge Discovery in Databases, method incorporating, in the data mining phase, machine learning algorithms from WEKA software. Results: The study highlights the association between low birth weight and reinforces its maintenance as an isolated criterion for the identification of children vulnerable to death in children under one year old. Despite the gaps found in the database of the mortality base system, it shows that this is a potential source of information for detecting factors associated with infant mortality. Final Considerations: Computational algorithms are effective to carry out associations, clusters and predictions effectively on health data, however, the database needs to be fed with reliable data regularly.